DATA MANAGEMENT AND BIOSTATISTICS CORE: SUMMARY In the management of biomedical data, features such as controlled access and audit trail are important in ensuring data quality, but establishing such a system requires capacity not just in proximate technical terms ? hardware, software and personnel ? but also in terms of organization and infrastructure. Although mobile applications can facilitate rapid and more community-based data collection, their use of cloud storage and other features can add complexity. Sequencing and ?omics? technologies generate large amounts of complex data which pose new challenges including ensuring traceability, and standardizing metadata and data flow across protocols. New quantitative methods have been required for reliable analysis of such data including standardized quality control protocols, new concepts such as the false discovery rate, and identification of signaling and metabolic pathways. Geostatistical and niche models have the potential to predict areas of high risk of infectious diseases but they require the collection, pre-processing and superposition of detailed data from multiple sources, such as satellites, digital elevation models and disease surveillance, as well as advanced statistical methods such as maximum entropy analysis and modeling of spatial autocorrelation. The goal of this Data Management and Biostatistics Core is to ensure that data from the research projects are accurate, secure, and comply with applicable standards, and to support their quantitative analysis, including bioinformatics and statistics. To achieve this, we will develop the following specific aims. 1) Write, in collaboration with project investigators, detailed statistical analysis plans (SAPs) and Data Management Plans (DMPs) in accordance with their aims and with international standards. 2) Ensure each project?s data are analyzed quantitatively in accordance with its SAP. 3) Enhance CIDEIM data management capacity to more closely comply with ECRIN requirements, and ensure each project follows its DMP. 4) Enhance existing mHealth applications for presumptive diagnosis (clinical prediction rule) and treatment follow-up to include estimation of adverse drug reactions and final cure status. The resulting data and analysis will further the proposal?s global aim of optimizing surveillance and treatment for control of cutaneous leishmaniasis